Higher-order MRF Research Articles

Ultrasound image restoration based on a learned dictionary and a higher-order MRF

The restoration of images degraded by blur and multiplicative noise is a critical preprocessing step in medical ultrasound images which exhibit clinical diagnostic features of interest. This paper proposes a novel non-smooth non-convex variational model for ultrasound images denoising and deblurring motivated by the successes of sparse representation of images and FoE based approaches. Dictionaries are well adapted to textures and extended to arbitrary image sizes by defining a global image prior, while FoE image prior explicitly characterizes the statistics properties of natural image. Following these ideas, the new model is composed of the data-fidelity term, the sparse and redundant representations via learned dictionaries, and the FoE image prior model. The iPiano algorithm can efficiently deal with this optimization problem. The new proposed model is applied to several simulated images and real ultrasound images. The experimental results of denoising and deblurring show that proposed method gives a better visual effect by efficiently removing noise and preserving details well compared with two state-of-the-art methods.

Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF

Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.

Disclose Variation in Synthetic Aperture Radar Images Adopting Fuzzy Clustering Along with Higher-order MRF Energy Function and Wavelet Denoising Technique - A Detailed Survey

Disclose Variation in Synthetic Aperture Radar Images Adopting Fuzzy Clustering Along with Higher-order MRF Energy Function and Wavelet Denoising Technique - A Detailed Survey

A Hypergraph-Based Reduction for Higher-Order Binary Markov Random Fields.

Higher-order Markov Random Fields, which can capture important properties of natural images, have become increasingly important in computer vision. While graph cuts work well for first-order MRF's, until recently they have rarely been effective for higher-order MRF's. Ishikawa's graph cut technique [1], [2] shows great promise for many higher-order MRF's. His method transforms an arbitrary higher-order MRF with binary labels into a first-order one with the same minima. If all the terms are submodular the exact solution can be easily found; otherwise, pseudoboolean optimization techniques can produce an optimal labeling for a subset of the variables. We present a new transformation with better performance than [1], [2], both theoretically and experimentally. While [1], [2] transforms each higher-order term independently, we use the underlying hypergraph structure of the MRF to transform a group of terms at once. For n binary variables, each of which appears in terms with k other variables, at worst we produce n non-submodular terms, while [1], [2] produces O(nk). We identify a local completeness property under which our method perform even better, and show that under certain assumptions several important vision problems (including common variants of fusion moves) have this property. We show experimentally that our method produces smaller weight of non-submodular edges, and that this metric is directly related to the effectiveness of QPBO [3]. Running on the same field of experts dataset used in [1], [2] we optimally label significantly more variables (96 versus 80 percent) and converge more rapidly to a lower energy. Preliminary experiments suggest that some other higher-order MRF's used in stereo [4] and segmentation [5] are also locally complete and would thus benefit from our work.

A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models.

We present a very general algorithm for structured prediction learning that is able to efficiently handle discrete MRFs/CRFs (including both pairwise and higher-order models) so long as they can admit a decomposition into tractable subproblems. At its core, it relies on a dual decomposition principle that has been recently employed in the task of MRF optimization. By properly combining such an approach with a max-margin learning method, the proposed framework manages to reduce the training of a complex high-order MRF to the parallel training of a series of simple slave MRFs that are much easier to handle. This leads to a very efficient and general learning scheme that relies on solid mathematical principles. We thoroughly analyze its theoretical properties, and also show that it can yield learning algorithms of increasing accuracy since it naturally allows a hierarchy of convex relaxations to be used for loss-augmented MAP-MRF inference within a max-margin learning approach. Furthermore, it can be easily adapted to take advantage of the special structure that may be present in a given class of MRFs. We demonstrate the generality and flexibility of our approach by testing it on a variety of scenarios, including training of pairwise and higher-order MRFs, training by using different types of regularizers and/or different types of dissimilarity loss functions, as well as by learning of appropriate models for a variety of vision tasks (including high-order models for compact pose-invariant shape priors, knowledge-based segmentation, image denoising, stereo matching as well as high-order Potts MRFs).

A homography transform based higher-order MRF model for stereo matching

Stereo matching is one of the most important and fundamental topics in computer vision. It is usually solved by minimizing an energy function, which includes a data term and a smoothness term. The data term consists of the matching cost, and the smoothness term encodes the prior assumption that the surfaces are piecewise smooth. In contrast to the traditional methods, in which the smoothness term is modeled by the pairwise interactions, the smoothness term is modeled with a higher-order model in this paper. With the prior assumption that a tiny piece of a smooth surface is approximately planar, a higher-order potential function based on the homography transformations is presented. Then the energy function defined on a factor graph is proposed, in which the coefficients of the factors depend on the color information of the input images so that the discontinuous edges are preserved. The belief propagation (BP) algorithm is adopted to minimize the energy function, and the experimental results tested on the Middlebury data set show the potential of the proposed method.

Adaptive graph-cuts algorithm based on higher-order MRF for video moving object detection

A new graph-cuts algorithm for video moving object detection is presented. Using the area, perimeter and shape of the moving objects, the energy function from the low-order Markov random field (LMRF) model is extended to the HMRF model, and the approximate graph-cuts algorithm based on this HMRF model is derived. Experimental results show that the proposed method is more effective than the traditional graph-cuts algorithm for video moving object detection.

High-Order MRF Prior Based Bayesian Deblurring

(1) State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China; (2) State Key Laboratory of Software Engineering Computer School Wuhan University China

Transformation of General Binary MRF Minimization to the First-Order Case

We introduce a transformation of general higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we formalize a framework for approximately minimizing higher-order multi-label MRF energies that combines the new reduction with the fusion-move and QPBO algorithms. While many computer vision problems today are formulated as energy minimization problems, they have mostly been limited to using first-order energies, which consist of unary and pairwise clique potentials, with a few exceptions that consider triples. This is because of the lack of efficient algorithms to optimize energies with higher-order interactions. Our algorithm challenges this restriction that limits the representational power of the models so that higher-order energies can be used to capture the rich statistics of natural scenes. We also show that some minimization methods can be considered special cases of the present framework, as well as comparing the new method experimentally with other such techniques.

Minimization of Monotonically Levelable Higher Order MRF Energies via Graph Cuts

A feature of minimizing images of submodular binary Markov random field energies is introduced. Using this novel feature, the collection of minimizing images of levels of higher order, monotonically levelable multi label MRF energies is shown to constitute a monotone collection. This implies that these minimizing binary images can be combined to give minimizing images of the multi label MRF energies. Thanks to the graph cuts framework, the mentioned class of binary MRF energies is known to be minimized by maximum flow computations on appropriately constructed graphs. With the aid of these developments an exact and efficient algorithm to minimize monotonically levelable multi label MRF energies of any order, which is composed of a series of maximum flow computations, is proposed and an application of the proposed algorithm to image denoising is given.